Distributed real-time security monitoring and alerting

ABSTRACT

Systems and methods are disclosed for distributed real-time security monitoring and alerting. The methods include transmitting a selected portion of biometrics data as a watchlist to each worker unit. The portion of biometrics data is selected in response to respective characteristic data received from each worker unit. Facial recognition data is received from each worker unit. The facial recognition data includes a person of interest with an associated match confidence value calculated by each worker unit based on respective watchlists received by each worker unit. A combined match confidence value is calculated between a same person of interest identified in multiple facial recognition data received from each worker unit and the biometric data associated with an individual. The combined match confidence value is calculated in response to match confidence values associated with the same person of interest in respective facial recognition data being below a match confidence threshold.

RELATED APPLICATION INFORMATION

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/816,444 filed on Mar. 11, 2019, incorporated herein by referencein its entirety.

BACKGROUND Technical Field

The present invention relates to security monitoring and moreparticularly to distributed real-time security monitoring and alerting.

Description of the Related Art

Security systems utilizing imaging devices are used to secure locationsand identify persons of interest. Processing of video captured byimaging devices located throughout a facility or public space isperformed at a central monitoring location to identify faces withindividuals listed in a watchlist. However, a centralized processingapproach prevents easy scaling of imaging device. Adding new imagingdevices increases the workload at the central monitoring location. Thus,performance can be quickly degraded if a large number of imaging devicesare installed.

SUMMARY

According to an aspect of the present invention, a method is providedfor distributed real-time security monitoring and alerting. The methodincludes transmitting a selected portion of biometrics data as awatchlist to each worker unit. The portion of biometrics data isselected in response to respective characteristic data received fromeach worker unit. Additionally, facial recognition data is received fromeach worker unit. The facial recognition data includes a person ofinterest with an associated match confidence value calculated by eachworker unit based on respective watchlists received by each worker unit.A combined match confidence value is calculated between a same person ofinterest identified in multiple facial recognition data received fromeach worker unit and the biometric data associated with an individual,the combined match confidence value being calculated in response tomatch confidence values associated with the same person of interest inrespective facial recognition data being below a match confidencethreshold.

According to another aspect of the present invention, a method isprovided for distributed real-time security monitoring and alerting inwhich characteristic data identifying at least a location and monitoringfocus of a worker unit is transmitted to a management server. At leastone watchlist is received by the worker unit from the management server.The watchlist includes biometric data of individuals representing aportion of biometric data managed by the management server selectedbased on the transmitted characteristic data. A person of interest isidentified using facial recognition based on the biometric data of thewatchlist received from the management server. Additionally, the personof interest is labeled in the image data including calculating a matchconfidence value; and an alert request including the image data havingthe labeled person of interest is transmitted to the management server.

According to yet another aspect of the present invention, a system isprovided for distributed security monitoring. The system includes one ormore worker units having an imaging system, and positioned to monitorindividuals in a space, and a management server. The management serveris configured to transmit a selected portion of biometrics data as awatchlist to each worker unit. The portion of biometrics data isselected in response to respective characteristic data received fromeach worker unit. Additionally, the management server is configured toreceive facial recognition data from each worker unit. The facialrecognition data includes a person of interest with an associated matchconfidence value calculated by each worker unit based on respectivewatchlists received by each worker unit. The management servercalculates a combined match confidence value between a same person ofinterest identified in multiple facial recognition data received fromeach worker unit and the biometric data associated with an individual inresponse to match confidence values associated with the same person ofinterest in respective facial recognition data being below a matchconfidence threshold. An alert is issued in response to at least one ofa match confidence value or a combined match confidence value at leastequaling the match threshold.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures.

FIG. 1 is a block representation illustrating a distributed monitoringand alerting system, in accordance with the present invention.

FIG. 2 is a block representation of an imaging system, in accordancewith an embodiment of the present invention.

FIG. 3 is a block representation of a face detection unit, in accordancewith an embodiment of the present invention.

FIG. 4 is a block representation of a face matching unit, in accordancewith an embodiment of the present invention.

FIG. 5 is a block representation of a face annotation unit, inaccordance with an embodiment of the present invention.

FIG. 6 is a block representation of an alerts modifier, in accordancewith an embodiment of the present invention.

FIG. 7 is a block representation of an alerts manager, in accordancewith an embodiment of the present invention.

FIG. 8 is a flow diagram illustrating an operational process executed bya watchlist loader module, in accordance with an embodiment of thepresent invention.

FIG. 9 is a flow diagram illustrating a watchlist download processexecuted by a watchlist loader module, in accordance with an embodimentof the present invention.

FIG. 10 is a flow diagram illustrating a notifications retrieval processexecuted by a biometrics input block, in accordance with an embodimentof the present invention.

FIG. 11 is a flow diagram illustrating operational process executed by afeature matcher module, in accordance with an embodiment of the presentinvention.

FIG. 12 is a flow diagram illustrating an operational process executedby a combiner module, in accordance with an embodiment of the presentinvention.

FIG. 13 is a flow diagram illustrating an operational process executedby an annotation module in accordance with an embodiment of the presentinvention.

FIG. 14 is a flow diagram of a worker unit registration process inaccordance with an embodiment of the present invention.

FIG. 15 is a flow diagram of a background worker unit process inaccordance with an embodiment of the present invention.

FIG. 16 is a flow diagram of a master unit process in accordance with anembodiment of the present invention

FIG. 17 illustrates an environment implementing a security system inaccordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

In accordance with embodiments of the present invention, systems andmethods are provided for distributed real-time monitoring and alerting.

In one embodiment, a first component, designated as a master unit,implements and manages one or more watchlists of individuals andbiometric data (facial information, for example) related to theindividuals. A plurality of second components, designated as workerunits, are coupled to imaging devices, such as security cameras. Eachworker unit processes image data (e.g., video feeds) received from aconnected camera to isolate regions of interest, e.g., regions includingfaces or partial faces, match any detected faces against the biometricdata received from the master unit, and label each face beforetransmitting the facial data to the master unit. Upon receivingannotated facial data, the master unit can issue an alert notifyingsecurity personnel of a detection of an individual from a watchlist.

By having worker units performing the image processing, data traffic onthe network can be minimized. Video feeds do not need to be transmittedfrom a plurality of cameras to a single processing unit. Instead, eachcamera can, in some embodiment, be directly coupled to a worker unit.Moreover, each worker unit can be coupled to an individual camera, thusthe workload for each worker unit can be easily maintained withinoptimal capabilities of the processing resources. Consequently, thesystem can be easily scaled with more worker/camera units while stillmaintaining processing efficiency.

Additionally, in some embodiments, the distributed monitoring system canbe configured to detect individuals that are not on a watchlist. Forexample, an embodiment can be configured with a biometric database of agroup of individuals authorized to enter a particular secured location.These authorized individuals are included in a watchlist. The workerunits monitoring the secured location can be configured, in thisembodiment, to disregard individuals on the watchlist and insteadidentify individuals that are not on the watchlist. In this way, thepresent embodiment can facilitate access to a secured location to onlyauthorized individuals while preventing access to non-authorizedindividuals. In the present embodiment, worker units monitoringdifferent secured areas can be provided with different watchlistcorresponding to the individuals authorized to enter the respectivesecured areas.

In some embodiments, the master unit maintains a database of biometricdata associated with one or more individuals. The biometric data can beselected by a biometrics manager of the master unit based oncharacteristics of each worker unit. The selected biometrics data can betransmitted as a watchlist to the respective worker units. Thus, eachworker unit can have watchlists that are customized based on each workerunit's particular location and monitoring focus.

In this way, a worker unit tasked with monitoring a secured space can beprovided with biometric data of individuals authorized to enter thesecured space, such as, for example, employees; these individuals canthus be ignored by the worker unit. Conversely, alerts can be triggeredwhen an individual that is not on the watchlist attempts to enter thesecured space. Additionally, not all employees may have the same levelof access, thus worker units monitoring one secured space can have adifferent watchlist than worker units monitoring a second secured spaceeven if the spaces are associated with the same institution.

Also, as the biometrics database is updated, as when individuals areadded or removed, for example, that may cause changes to the customizedwatchlists sent to each worker unit, the master unit can initiatetransfer of the updated watchlists to the appropriate worker units. Thisfeature can facilitate rapid, substantially instantaneous, updating ofwatchlists at all worker units when changes are made to the biometricsdatabase.

In addition to location and monitoring focus, other characteristics ofan individual worker unit can be used as characteristics for determiningwhich biometric data to include in a watchlist. For example, thecapabilities of the imaging system, (infrared, monochrome or colorimaging, high pixel count, etc.), and available resources (free storagespace, processor bandwidth, etc.), etc. In some embodiments, the workerunit provides the characteristics to the master unit during initialregistration of the worker unit, and henceforth the worker unit canprovide a unique identifier that the master unit associates with thecharacteristics.

For simplicity, individuals that trigger an alert will be identified aspersons of interest hereinafter. Thus, in one embodiment whereindividuals on a watchlist trigger an alert, those watchlistedindividuals are referred to hereinafter as persons of interest. Inembodiments where individuals not listed on a watchlist trigger alerts,those non-watchlisted individuals are referred to hereinafter as personsof interest, as well. Non-watchlisted individuals can, in someembodiments, trigger alerts in situations where the watchlist includesbiometric information of individuals, such as, for example, employees,that are authorized to enter an area. In such a case individuals not onthe watchlist are not authorized to enter the area and thus if detectedwould trigger an alert.

In some embodiments, where multiple worker units are present andmonitoring the same general area, perhaps from different angles orperspectives, a facial match of the same individual made by each of themultiple worker units can be combined at the master unit to increase thematch confidence level. As each worker unit has a different view of theindividual, some views may yield a better, or higher confidence, matchof the individual then others. Additionally, the different angles andperspectives can bring different facial features into focus. Forexample, features obscured from view at one angle can be clear fromanother angle. Thus, by combining the various views at the master unitthe number of matching biometric elements can be increased, resulting inan increased match confidence for the individual. Accordingly, while,individually, each worker unit may arrive at a somewhat low confidencematch (e.g., a match confidence below a threshold value), thecombination of matched features from the multiple worker units canincrease the confidence level of the match above the threshold value. Insome embodiments, the combining of the low confidence matches of themultiple worker units is performed by the master unit as each match isreceived from the respective worker unit.

In embodiments where the match confidence can be increased by combiningfacial features from multiple facial matches, the worker units can havea match confidence threshold set at a lower value than the matchconfidence threshold set at the master unit. For example, the workerunits can have a match confidence threshold set at a 0.65 value, whilethe master unit can have a match confidence threshold set at a 0.70value. In this way, the worker units can be configured to presentpersons of interest to the master unit even though the match confidenceis not at the level that would trigger an alert. The master unit, havingreceived facial features for person of interest with lower matchconfidences from multiple sources (e.g., worker units), can furtheranalyze the facial features of the person of interest by combining thefacial features from the multiple sources. The master unit can continueto add additional facial feature data for the person of interest as newdata is received from the worker units until the combined matchconfidence value of the person of interest equals or exceeds the highermatch confidence threshold of the master unit.

FIG. 1 illustrates a block representation of a distributed securitymonitoring system 100 in accordance with an embodiment of the presentinvention. The distributed monitoring and alerting system 100 includes amaster unit 102 coupled to one or more worker units 160 via a network150. The network 150 can be a local area network (LAN), wide areanetwork (WAN), Internet, or a combination of these. Additionally, thenetwork 150 can be configured using any of the IEEE 802 family ofnetworking protocols, for example, such as Ethernet (IEEE 802.3),Wireless LAN (IEEE 802.11), etc. and transmit data using, for example,Transmission Control Protocol (TCP), User Datagram Protocol (UDP), etc.

The master unit 102 includes a processor 104, such as, a centralprocessing unit (CPU), a graphical processing unit (GPU), a configuredfield programmable gate array (FPGA), an application specific integratedcircuit (ASIC), or a combination of these, for example. The processor104 is in electrical communication, via a system bus 120, with memorydevices, such as random-access memory (RAM) 106, and read-only memory(ROM) 108. The system bus 120 is also coupled to one or more massstorage devices 110, such as, for example, hard drives, solid statedrives, etc. A display 112, user interface controller 116 and networkinterface 114 are also coupled to the system bus 120. The user interfacecontroller 116 provides an interface between the master unit 102 and auser by way of various human interface devices (HID) such as, forexample, keyboard 142, mouse 144, speakers 140, etc. The networkinterface 114 transmits and receives data communication between themaster unit 102 and the worker units 160 via the network 150. Thestorage device 110 includes a biometric database 128 and an alertsmanager database 130.

In some embodiments, the processor 104 functions as an alerts manager122, an alerts modifier 124 and a biometrics manager 126, as describedin further detail below. The alerts manager 122, the alerts modifier 124and the biometrics manager 126 are also referred to collectively hereinas master application instances. In embodiments where the processor 104is a CPU, the alerts manager 122, the alerts modifier 124 and thebiometrics manager 126 are implemented as program code stored on themass storage device 110, in RAM 106, in ROM 108 or in a combination ofthese, and executed by the CPU. In other embodiments, the processor 104is an FPGA that is configured to implement the alerts manager 122, thealerts modifier 124 and the biometrics manager 126 by way ofparticularly configured gates, thus implementing the functions inhardware. In other embodiments the alerts manager 122, the alertsmodifier 124 and the biometrics manager 126 can be implemented using acombination of hardware and program code.

In some embodiments, the worker units 160 communicate with the masterunit 102 over the network 150 using a network interface 170. Similar tothe master unit 102, the worker units 160 include a system bus 162providing electrical communication between a processor 164, RAM 166, ROM168, mass storage device 182, the network interface 170, a userinterface controller 174, and an imaging system 172.

In some embodiments, the processor 164 functions as a face annotationunit 176, a face matching unit 178 and a face detection unit 180, asdescribed in further detail below. The face annotation unit 176, theface matching unit 178 and the face detection unit 180 are also referredto collectively herein as worker application instances. In embodimentswhere the processor 164 is a CPU, the face annotation unit 176, the facematching unit 178 and the face detection unit 180 are implemented asprogram code stored on the mass storage device 182, in RAM 166, in ROM168 or in a combination of these, and executed by the CPU. In otherembodiments, the processor 164 is an FPGA that is configured toimplement the face annotation unit 176, the face matching unit 178 andthe face detection unit 180 by way of particularly configured gates,thus implementing the functions in hardware. In other embodiments theface annotation unit 176, the face matching unit 178 and the facedetection unit 180 can be implemented using a combination of hardwareand program code.

In some embodiments, the imaging system 172 (e.g., digital video camera)can include an image sensor having a plurality of pixel elementsarranged in a two-dimensional array. The pixel elements can beimplemented as charge-coupled devices (CCD), or as complementarymetal-oxide semiconductor (CMOS) elements. Additionally, the imagingsystem 172 can be configured to provide still images, video feeds orboth. In some embodiments, the worker unit 160 can be equipped with oneimaging system 172. However, other embodiments can include multipleimaging systems 172, with necessary computational resources scaledaccordingly. Additionally, in some embodiments the worker unit 172 isconnected to one or more imaging systems 172 by way of externalinterface ports (not shown), such as high definition multimediainterface (HDMI), universal serial bus (USB) or a combination thereof,for example. Other embodiments can include a worker unit 172 and animaging system 172 integrated into a unitary body.

As shown in FIG. 1, an embodiment includes a master unit 102 and one ormore worker units 160 in communication with the master unit 102 via thenetwork 150. The master unit 102 is configured to provide watchlistmanagement (via the biometrics manager 126) and alerts management (viathe alerts manager 122 and the alerts modifier 124). Each worker unit160, on the other hand, is configured to perform the image capture (viathe imaging system 172), image analysis (via the face annotation unit176, the face matching unit 178 and the face detection unit 180), andalerting for persons of interest.

In some embodiments, the worker unit 160 receives a watchlist whenupdates to the watchlist are available from the master unit 102. Thus,since the worker unit 160 includes a copy of the watchlist, the workerunit can continue to perform facial identification and matching based onthe internally stored watchlist even when network communication issuspended or interrupted, for example, as can occur during a poweroutage or network equipment fault. Once network connectivity between theworker unit 160 and the master unit 102 is reestablished, any persons ofinterest detected by the worker unit can be transmitted to the masterunit 102 for further action. Thus, the distributed system disclosedherein can function even with intermittent network connectivity.

In some embodiments, the system 100 depicted in FIG. 1 implements ausecase topology in which blocks representing various micro-services areinter-connected to perform the real-time monitoring and alerting of thepresent invention. In the usecase topology, input blocks provide dataacquisition micro-services and modules implement processingmicro-services.

In some embodiments, the usecase topology includes the alerts manager122, the alerts modifier 124 and the biometrics manager 126 asapplication instances deployed by the master unit 102, and the faceannotation unit 176, the face matching unit 178 and the face detectionunit 180 as application instances deployed on each worker unit 160.

In some embodiments, distributed security monitoring system 100 includesone or more worker units 160 (e.g., worker unit (1) and worker unit (N))that are positioned to monitor individuals in a space. The worker units160 can be positioned to monitor overlapping areas of the space.Alternatively, the worker units 160 can be positioned to monitor spacesisolated from one another. A management server, such as, for example,the master 102, includes a biometrics manager 126. The biometricsmanager 126 is configured to receive characteristic data from the one ormore worker units 160.

The characteristic data identifies at least a location and monitoringfocus of a worker unit 160. The location of the worker unit 160 can beexpressed as geographical coordinates (e.g., longitude, latitude,elevation, and orientation with respect to magnetic North), thusproviding an absolute position of the worker unit 160. Alternatively,the location of the worker unit 160 can be expressed as a relativeposition (e.g., facing entrance, conference room 1, etc.). Whileexpressing location of a worker unit 160 using relative position can beless precise, the relative position can be more relevant andunderstandable to a security officer, especially when the worker units160 are located within a building or other well defined area, such as apark, or street intersections, for example.

The monitoring focus can include an identifier indicating whether themonitored space is a public area or a restricted area. In a restrictedarea, only authorized individuals are permitted to enter, thus amonitoring focus on a restricted, or secured area can be used generate awatchlist customized to include individuals authorized to be in thesecured area. Since in a public space all individuals are by defaultpermitted to enter, a monitoring focus on a public space can be used togenerate a customized watchlist that includes individuals that are notpermitted to be in the area, such as a criminal or the like.Additionally, the monitoring focus of the worker unit 160, determineswhether persons of interest are individuals listed in the watchlist(e.g., public space focus) or the persons of interest are individualsexcluded from the watchlist (e.g., secured space focus).

Based on the received characteristic data, the biometric manager 126 canbe configured to select biometrics data associated with individuals thatsatisfy the monitoring focus criteria and location of the worker unit160 from the biometrics database 128. Additionally, the biometricmanager 126 can be configured to generate a customized watchlist foreach worker unit 160 that includes the selected biometrics data, andtransmit the customized watchlist to the appropriate worker unit 160.Also, the biometric manager 126 can be configured to update each workerunit 160 whenever changes are made to the biometrics database 128 thataffect the content of the customized watchlists. In some embodiments,the master unit 102 can initiate transmission of watchlist updates tothe appropriate worker units 160 when changes are made to the biometricsdatabase 128. In other embodiments, the worker units 160 periodicallytransmit update requests to the master unit 102, which can transmit anywatchlist updates in response. Having the worker units 160 initiateupdates by transmitting update requests to the master unit 102 canprovide a robust watchlist updating system even when network addressesare dynamically assigned to the worker units 160, and/or when networkconnectivity between the master unit 102 and the worker units 160 isintermittent.

The alerts manager 122, in some embodiment, can be configured to receivefacial recognition data from the one or more worker units 160. Thefacial recognition data can identify at least one person of interestwith a match confidence value based on the customized watchliststransmitted to the respective worker units 160. The alerts manager 122can also issue an alert in response to at least one match confidencevalue exceeding a threshold value. The adjustment to the threshold valueallows modification of the sensitivity of the monitoring system.

In cases where each individual worker unit 160 is unable to match anindividual (person of interest) to a match confidence exceeding thethreshold value, the alerts manager 122 can calculate a combined matchconfidence value of the person of interest identified by each of therespective worker units 160 using the biometric data associated with theindividual. Since each worker unit 160 can provide a different angle, orperspective, of the individual, the combined facial recognition datafrom the plurality of worker units 160 can include more facial featuresfor matching against the biometric data in the biometrics database 128than any one worker unit 160 can provide, thus increasing the overallmatch confidence. The combined match confidence value can be calculatedusing multiple facial recognition data of the same person of interestprovided by different worker units 160 within a predetermined timeinterval. Subsequently, the alerts manager 122 can issue an alert inresponse to the combined match confidence value exceeding the thresholdvalue. In some embodiments, the combined match confidence value can becalculated using multiple facial recognition data received from the sameworker unit 160. Each facial recognition data can include data fromdifferent image frames such that the person of interest is recorded fromdifferent angles in each image frame, as may occur, for example when theperson of interest is turning.

When the combined match confidence value is calculated using multiplefacial recognition data from the same worker unit 160, the combinedmatch confidence value can be calculated by the worker unit 160. In thiscase, the combined match confidence value can be transmitted to themaster unit 102 as a match confidence value. The facial recognition datatransmitted to the master unit 102 can include the image frames used tocalculate the combined match confidence value.

In some embodiments the application instances deployed by the masterunit 102 are marked as “uniqueSiteWide”, where instances are implementedas single instances deployed site-wide and reused by each of the workerapplication instances. Thus, in these embodiments, multiple instances ofthe face annotation unit 176, the face matching unit 178 and the facedetection unit 180 (for each worker unit 160) interact with a singleinstance of each of the alerts manager 122, the alerts modifier 124 andthe biometrics manager 126 deployed by the master unit 102. In this way,the resource needs of the master unit 102 remain unchanged regardless ofthe number of worker units 160 installed in the system 100. Moreover,since each worker unit 160 manages video feeds from dedicated imagingsystems 172, the system 100 can be scaled to include any number of videofeeds by installing additional worker units 160, thus, the resourceneeds for each worker unit remains unchanged as well.

Within the usecase topology, illustrated in FIGS. 2-7, each imagingsystem 172 includes a camera 202 or other imaging sensor, and a camerainput block 204, that segments the video stream from the camera andoutputs frames 206, as shown in FIG. 2.

Referring to FIG. 3, the frames 206 from the camera input block 204 areprovided to a frame input block 302 of the face detection unit 180. Aregion of interest (ROI) filter module 304 receives frames from theframe input block 302, and filters regions of interest within the frame.Unwanted portions of the frame are discarded, and a transformed frame isgenerated that includes only the region of interests of the frame. Thetransformed frame is transmitted to face detector modules 306. The facedetector module 306 can be deployed as multiple application instancesexecuting in parallel. Each face detector module 306 receives atransformed frame from the ROI filter module 304. The face detectormodule 306 detects faces in the received transformed frame and extractsfacial features of the faces. The detected faces along with thecorresponding facial features from all the face detector modules 306 arecollected by an output module 308. The output module 308 publishes thecollected data as detected face data 310 for further use by othermodules.

Turning to FIG. 4, in an embodiment, face data is received by a faceinput block 402 of the face matching unit 178. Additionally, the facematching unit 178 includes a biometrics input block 404 configured toreceive biometrics data 406 (e.g., watchlists) from the biometricsmanager 126, and a requests input block 408 configured to receiverequests 410 for querying loading status of watchlists and for actualloading of watchlists.

In some embodiments, the requests input block 408 can be implementedusing, for example, representational state transfer (REST) applicationprogramming interfaces (API). If the request 410 is to load watchlists,then the requests input block 408 issues a loading request to instancesof a watchlist loader module 412. If the request is for querying theloading status, then the requests input block 408 retrieves and outputsthe loading status of the watchlists. The status of watchlist loading ismaintained by the face matching unit 178 in a global hash table 414.

In some embodiments, the biometrics input block 404, upon systemstart-up, issues a watchlist loading request to watchlist loader module412 instances. The biometrics input block 404 also connects to thebiometrics manager 126, receives notifications 406 and issuesnotifications to feature matcher module 416 instances. Thesenotification can include, e.g., making changes (e.g., add/update/remove)to a set of watchlists, making changes (e.g., add/update/remove) toindividuals in a watchlist, turning a watchlist monitor ON or OFF,re-downloading a watchlist, and synchronizing watchlist and worker unitstatus updates, etc.

As noted above, the watchlist loader module 412 can have multipleinstances running in parallel to address watchlist loading requests. Thewatchlist loader module 412 instances can connect to the biometricsmanager 126 to download watchlists 426 along with biometrics details inthe watchlists 426. The biometrics details of each individual in thewatchlists 426 can include facial features. The watchlist loader moduletransmits the biometrics details to feature matcher module 416 instancesusing a person identifier (ID), thus all updates for a particularindividual can be passed to the same instance of the feature matchermodule 416 that received the first update for the individual. Theloading status of each watchlist 426 is updated by watchlist loadermodule 412 instances in the global hash table 414.

The feature matcher module 416 can have multiple instances running inparallel, with each instance loading a portion of a watchlist 426,including biometric details. Using the portion of the watchlist 426loaded, each instance of the feature matcher module 416 analyzes areceived face, and issues match results to a combiner module 418. Eachof the feature matcher module 416 instances receive the same face foranalysis, thus the analysis of the face can occur in parallel againstthe individuals in a watchlist, thereby reducing processing time formatching a face to a watchlisted individual.

The combiner module 418 receives partial match results from each of thefeature matcher module 416 instances, and computes the best match resultfrom the partial matches. If a facial match is found, the combinermodule 418 transfers the facial match data 420 to the alerts modifier124 and the output module 422 which, in turn, transmits the facial matchdata 424 to the face annotation unit 176. In some embodiments, a facematch alert (in the form of a facial match data 420) is transmitted tothe alerts modifier 124 each time a facial match of an individual isdetected. In some embodiments, alerts for matched faces (facial matchdata 420) are sent to the alerts modifier 124 when the time differencebetween the current time and the time of a previously sent alert (facialmatch data 420) for the same individual exceeds a specified timeinterval. In other embodiments, alerts for matched faces (facial matchdata 420) are sent to the alerts modifier 124 if the current match scoreis greater than a previous match score and a predefined delta change.

FIG. 5 illustrates an embodiment of a face annotation unit 176. The faceannotation unit 176 receives facial match data 424 from the outputmodule 422 of the face matching unit 178. The facial match data 424includes image frames, detected faces and matched faces from which theface annotation unit 176 generates annotated frames 520 as an output ofan annotation module 518. Additionally, the face annotation unit 176transmits alerts for people who are not in the watchlist (unmatched facedata 524).

In some embodiments, the face annotation unit 176 includes a frame inputblock 504 that is configured to receive video frames 206 from the camerainput block 204 shown in FIG. 2. The frame input block 504 transmits thevideo frames 206 to an annotation module 518. Also, a detection inputblock 502 receives detected face data 310 from the face detection unit180, which are transmitted to the annotation module 518 as well.Additionally, a match input block 506 receives facial match data 424from the face matching unit 178, which are transmitted to the annotationmodule 518.

In some embodiments, the annotation module 518 combines the datareceived from the frame input block 504, detection input block 502, andmatch input block 506, which are subsequently rendered and published asannotated frames 520. Further, the annotation module 518 sends facesthat do not match individuals in the watchlist to an output module 522.The output module 522 of the face annotation unit 176 receives faces ofindividuals that are not listed on the watchlist. The output module 522sends the unmatched faces (unmatched face data 524) to the alertsmanager 122.

FIG. 6 illustrates an embodiment of the alerts modifier 124 of themaster unit 102 (shown in FIG. 1). The alerts modifier 124 receivesalerts, modifies the alerts and sends them to the alerts manager 122.The alerts modifier 124 can include a JavaScript™ object notation (JSON)receiver block 602, a BSON (binary JSON) receiver block 604, an alertsmodifier module 606 and an output module 608. The JSON receiver block602 receives alerts in JSON format 610 and transmits those alerts toinstances of the alerts modifier module 606. Similarly, the BSONreceiver block 604 receives alerts in BSON format 612 and transmitsthose alerts to instances of the alerts modifier module 606.

The alerts modifier module 606 can implement multiple instances. Eachinstance receives each alert from the JSON receiver block 602 and theBSON receiver block 604. Additionally, the alerts modifier module 606retrieves an enrolled image of an individual from the biometrics manager126. The alerts modifier module 606 modifies the alert by attaching theenrolled image and sends the modified alert to the output module 608.The output module 608 receives the modified alert from alerts modifiermodule 606 and transmits the modified alert 614 received from the alertsmodifier module 606 to the alerts manager 122.

FIG. 7 illustrates an embodiment of an alerts manager 122 of the masterunit 102 (shown in FIG. 1). The alerts manager 122 can include a JSONreceiver block 702 that receives JSON formatted alerts 704 and transmitsthem to an alerts writer module 706 and an alerts publisher module 708.Similarly, the alerts manager 122 can include a BSON receiver block 710that receives BSON formatted alerts 712 and transmits them to the alertswriter module 706 and the alerts publisher module 708. The alerts writermodule 706 writes alerts received from the JSON receiver block 702 andthe BSON receiver block 710 into the alerts manager database 130. Insome embodiments, block 702 and block 710 can combine multiple facialrecognition data of the same person of interest when the matchconfidence value provided by different worker units 160 within apredetermined time interval is below the threshold value, and calculatea combined match confidence value that exceeds the threshold value. Thealerts manager 122 can calculate a combined match confidence value, andissue an alert in response to the combined matched confidence exceedingthe threshold value.

In some embodiments, the combined match confidence value is calculatedby identifying a plurality of facial recognition data that include thesame person of interest for which the match confidence value is belowthe match confidence threshold. The plurality of facial recognition datacan originate from the same worker unit, different worker units or acombination thereof. The facial features of the person of interest areextracted from the plurality of facial recognition data. Once extracted,the facial features are combined to generate a new facial feature of theperson of interest. This new facial feature is matched to biometricsdata stored in the biometrics database 128 to obtain a combined matchconfidence. In some embodiments, the process for performing the matchingis similar to the process implemented by the face matching unit 178(shown in FIG. 4) and face annotation unit 176 (shown in FIG. 5).However, rather than having a worker unit 160, execute the combinedfacial feature matching process, the process can be performed by themaster unit 102.

In some embodiments, the alerts manager database 130 stores all alertsfor a specified period of time. In other embodiments alerts are storedin the alerts manager database 130 until a system operator removes thealerts. The alerts publisher module 708 receives alerts from the JSONreceiver block 702 and the BSON receiver block 710 and publishes thealerts 614 in a manner facilitating usage by other applications and/orthird parties.

FIG. 8 illustrates a flowchart of an operational process executed by anembodiment of watchlist loader module 412 shown in FIG. 4. In someembodiments, multiple instances of the watchlist loader module 412 canbe implemented, with each instance of the watchlist loader module 412executing the operational process illustrated in FIG. 8. Upon receivingan input from the requests input block 408 or the biometrics input block404 shown in FIG. 4, the watchlist loader module 412, at block 801,extracts the watchlist identifier (watchlist ID) from the input. Atblock 803, the watchlist loader module 412 requests that a watchlistdata file corresponding to the watchlist ID be downloaded from thebiometrics manager 126 of the master unit 102 shown in FIG. 1. Thewatchlist loader module 412 determines, at block 805, whether therequested watchlist download was successful or not. An unsuccessfuldownload attempt causes the process to continue to block 815, where awatchlist loading status is set to “error”. From block 815, the processcontinues to block 813 where the watchlist loader module 412 waits forreceipt of a next input.

On the other hand, a successful download attempt causes the process toproceed from block 805 to 807, where the watchlist loading status is setto “success”. The watchlist loader module 412 proceeds to block 809 andreads the watchlist file. The records in the watchlist file aretransmitted, at block 811, to a feature matching module 416, such as,for example, the feature matching module 416 shown in FIG. 4. Thewatchlist loader module 412, upon completing transmission of the recordsin block 811, proceeds to block 813 and waits for a new input.

FIG. 9 illustrates a watchlist download process executed by instances ofthe watchlist loader module 412 in some embodiments. The watchlistloader module 412 begins the download process at block 901 by receivinga watchlist ID of a requested watchlist. The watchlist loader module412, at block 903, extracts a local notification ID from a local copy ofthe watchlist associated with the watchlist ID if the local copy of thewatchlist exists. If the local copy does not exist, the localnotification ID is set to a default value, such as, for example, −1,signifying that no local copy exists. This local notification ID is thelatest notification ID for the watchlist that was previously downloaded.After obtaining the local notification ID at block 903, the latestnotification ID for the watchlist is obtained from biometrics manager126 at block 905. The latest notification ID from the biometrics manager126 is referenced hereinafter as a master notification ID.

At block 907, the local notification ID and the master notification IDare compared and matched. If the local notification ID and the masternotification ID match at block 907, then there is no need to downloadanother copy of the watchlist, as the current local copy is the latestversion of the watchlist. Thus, the watchlist loader module 412 proceedsto block 909, where a “true” signal, signifying successful completion ofthe download process, is issued.

However, in cases where the local notification ID and the masternotification ID do not match at block 907, the watchlist loader module412 proceeds to block 911. At block 911, watchlist details aredownloaded from the biometrics manager 126. In some embodiments, thewatchlist details can include watchlist name, priority or threat level,and any other metadata associated with the watchlist. Alerts issued bythe system 100 can include the priority or threat level of the watchlistin which the person of interest is found.

A check is performed, at block 913, to determine whether download of thewatchlist details was successfully. If the download of the watchlistdetails was unsuccessful, the process continues to block 915, where thewatchlist loader module 412 checks if a copy of any previous version ofthe watchlist exists. If a previous watchlist file version exists, theprocess returns a “false” signal at block 917. The “false” signalindicates that the download process did not complete successfully, andthus, the watchlist version that was previously downloaded will be used.If a previous watchlist file version does not exist, the watchlistloader module 412 generates an error file including the watchlist ID atblock 919. The process continues to block 917 and returns a “false”signal indicating that the download process did not completesuccessfully. The presence of the error file signifies that download ofthe watchlist failed and needs to be re-tried later.

If the watchlist details are loaded successfully, the process continuesfrom block 913 to block 921. At block 921, the watchlist size isobtained from the biometrics manager 126. The watchlist size is thetotal number of individuals registered in the watchlist.

The watchlist loader module 412 instance checks if the size was obtainedsuccessfully or not. If the size is not successfully obtained, theprocess proceeds to block 915 and continues as described above. However,if the size is obtained successfully, then a temporary updated watchlistfile is created at block 925. The temporary updated watchlist filenamecan include, in some embodiments, a watchlist ID and ending with an“.updated” extension. Other filename naming formats can be used as well.Additionally, the temporary updated watchlist file can include themaster notification ID, therein. At block 927, the watchlist loadermodule 412 initializes an “offset” variable and a “limit” variable. Forexample, the “offset” variable can be set to a value of 0, and the“limit” variable can be set to a value of 20. A check is performed atblock 929, checking if “limit” is greater than the watchlist size. If“limit” is greater than the watchlist size, the watchlist size isassigned to “limit” at block 931. The process then continues to block933. However, if “limit” is not greater than the watchlist size, theprocess proceeds directly from block 929 to block 933, skipping overblock 931.

The watchlist loader module 412 retrieves “limit” number of records fromthe biometrics manager 126 starting at the current “offset” at block933. The watchlist loader module 412 verifies, at block 935, that therecords are successfully retrieved at block 933. If the records are notsuccessfully retrieved at block 933, the watchlist loader module 412instance proceeds to block 915 and continues as described above. If therecords are successfully retrieved, then the records are written in thetemporary updated watchlist file at block 937.

Once a batch of records is successfully obtained at block 933 andwritten to the temporary updated watchlist file at block 937, the“offset” is increase by “limit” and the “size” is reduced by “limit” atblock 939. A check is performed at block 941 to verify that “size” isgreater than 0, signifying that additional records still remain to bewritten to the temporary updated watchlist file. If the “size” is notgreater than 0, the process renames the temporary updated watchlist fileat block 943 by removing the “.updated” extension from the filename,leaving the watchlist ID as the filename. In other embodiments thetemporary updated watchlist file can be renamed using a predefinednaming format, such as, for example, replacing “.updated” with a datecode. The watchlist loader module continues from block 943 to block 909and ends.

However, if the “size” is greater than 0, signifying that more recordsexist to download, the process returns to block 933 where the new“offset” and “size” values are used to retrieve additional records, andcontinues as described above. The loop from block 933 to block 941continues until all the records in the watchlist are successfullydownloaded from the biometrics manager 126.

FIG. 10 illustrates a procedure followed by an embodiment of thebiometrics input block 404 (shown in FIG. 4) to retrieve notification(s)from the biometrics manager 126 (shown in FIG. 1).

In some embodiments, the biometrics input block 404 checks if anywatchlist error files exist at block 1001. If yes, the watchlist errorfile is obtained at block 1003. The biometrics input block 404 attempts,in block 1005, to download the watchlist identified in the watchlisterror file using the procedure described in FIG. 9, above. Thebiometrics input block 404 confirms at block 1007 that the watchlist issuccessfully downloaded. If the watchlist is successfully downloaded,the error file is removed at block 1009. Additionally, the biometricsinput block 404, upon successful download of the watchlist, can issue adownloaded notification for the watchlist at block 1011. The processreturns to block 1001 to begin the procedure again with a next watchlisterror file. If the watchlist is not downloaded successfully, processreturns to block 1001 to begin the procedure again with a next watchlisterror file, until no more watchlist error files exist.

Once the biometrics input block 404 has processed all the watchlisterror files, signified by block 1001 not finding any additionalwatchlist error files, the process proceeds to block 1013. At block1013, the biometrics input block 404 obtains local notification IDs forall watchlists. The biometrics input block 404 iteratively performsblock 1015 through block 1023, for each of the watchlists. At block 1015the biometrics input block 404 confirms that a watchlist correspondingto the current local notification ID exists. The next watchlist isaccessed at block 1017. Any notifications associated with the currentwatchlist received from the biometrics manager 126 since the most recentlocal notification ID are obtained at block 1019. If no notificationsexist for the watchlist, as determined at block 1021, the processreturns to block 1015, otherwise the process continues to block 1023.These new notifications are added to the local notifications and thelocal notification ID is updated, at block 1023, to the most recentnotification ID. These notifications are related to addition, update orremoval of a watchlist or individual. Upon completion of block 1023, theprocess returns to block 1015.

Once the notifications for the existing watchlists are added, thebiometrics input block 404 proceeds from block 1015 to block 1025, wherethe current local notification IDs for the watchlists are retrieved. Thebiometrics input block 404 iteratively performs block 1027 through block1035 for each of the watchlists. At block 1027, a check is performed todetermine if a watchlist exists to be processed. If the check finds thatadditional watchlists exist, biometrics input block 404 gets the nextwatchlist at block 1029. The biometrics input block 404 checks, at block1031 if a “synch” notification was sent for the watchlist. If a “synch”notification was sent, then the process returns to block 1027 and thenext watchlist is checked. If a “synch” notification was not sent, thebiometrics input block 404 checks, at block 1033, if the watchlist is insynch with the watchlist version held by the biometrics manager 126(e.g., the master watchlist). If the local notification ID and the mostrecent notification on the master watchlist obtained from the biometricsmanager 126 are the same, then a “synchronized” Watchlist notificationis added at block 1035. The process returns to block 1027 to process thenext watchlist. When no more watchlists remain to be processed, thebiometrics input block 404 proceeds to block 1037 where the watchlistfiles of deleted watchlists are removed. The biometrics input block 404checks, at block 1039, if any notifications have been added during theprocess shown in FIG. 10, namely at block 1011, block 1023 and block1035. If notifications exist, the biometrics input block 404 processesthe notifications at block 1041 one by one by forwarding eachnotification to the feature matcher module 416 or the watchlist loadermodule 412, or processing the notification locally, as appropriate.Notifications to “download” a watchlist are sent to instances of thewatchlist loader module 412, while all other notifications, exceptnotifications for “synchronized” watchlists, are sent to instances ofthe feature matcher module 416. Notifications for “synchronized”watchlist is locally processed by the biometrics input block 404, andthe loading status of the watchlist is updated to “success” in theglobal hash table 414.

If, at block 1039, no notifications exist or after processing allnotifications at block 1041, the biometrics input block 404 sleeps for aspecified time interval and then jumps to block 1001 and continues toobtain and process notifications from the biometrics manager 126.

Turning to FIG. 11, an operational process of an embodiment of thefeature matcher module 416 is shown. Upon receiving an input from any ofthe biometrics input block 404, the face input block 402 and thewatchlist loader module 412, the feature matcher module 416 extracts anyactions in the input at block 1101. An instance of the feature matchermodule 416 checks the extracted actions at block 1103 to determinewhether the actions are for an update or an addition to the watchlists.If the action is an update or addition action, the process continues toblock 1105. At block 1105, the feature matcher module 416 determines ifthe action includes a Person_ID, which identifies an individual in thewatchlist. If the action does not include a Person_ID, the featurematcher module 416 updates the watchlist details at block 1107. However,if the action includes a Person_ID the feature matcher module 416updates or adds an individual identified by the Person_ID at block 1109.Once the action is completed the process continues to block 1111 wherethe feature matcher module 416 waits for the next input.

Returning to block 1103, if the feature matcher module 416 determinesthat the action is neither an update nor an addition action, the featurematcher module 416 continues to block 1113. At block 1113, the processdetermines if the action is a removal action. If the action is a removalaction, the feature matcher module 416 determines if the action alsoincludes a Person_ID at block 1115. If the action does not include aPerson_ID, the feature matcher module 416 removes the watchlist detailsat block 1117. However, if the action includes a Person_ID the featurematcher module 416 removes the individual identified by the Person_ID atblock 1119. Once the action is completed the process continues to block1111 where the feature matcher module 416 waits for the next input.

Returning to Block 1113, the feature matcher module 416, if the actionis not a removal action, then the input contains a face to match. Thefeature matcher module 416 validates the face at block 1121. At block1123, the feature matcher module 416 initializes a Max_score variable to−1, a Person_ID variable to −1 and a Picture_ID variable to −1. Theprocess determines if facial features for matching exist at block 1125.If features to match exist, the process obtains a next feature alongwith the face's watchlist status at block 1127. The watchlist is checkedto determine if the watchlist status is set to “active” in block 1129.If the watchlist is not active, the process returns to block 1125 andcontinues as described above.

However, if the watchlist is determined to be active at block 1129, amatch threshold is obtained at block 1131. A facial match is performedbetween the current facial feature and facial data associated with thewatchlist at block 1133. During the facial match at block 1133, asimilarity score is generated reflecting the degree of similaritybetween the current facial feature and the facial data. A check isperformed to determine if the similarity score is greater thanMax_score, and if the similarity score is greater than the matchthreshold at block 1135. A similarity score less than Max_score and thematch threshold causes the process to return to block 1125 and continueas described above.

If at block 1135, the similarity score is determined to be greater thanthe Max_score and the match threshold, the process proceeds to block1137, where the Max_score, Person_ID and Picture_ID are updated. Oncethe Max_score, Person_ID and Picture_ID are updated, the process returnsto block 1125. The Max_score is updated to reflect the similarity score.The loop between block 1125 and block 1137 continues until no morefeatures exist to process. When block 1125 determines that no morfeatures exist, the process continues to block 1139. At block 1139, theprocess determines if the Max_score is greater than −1.

A Max_score greater than −1 signifies that a match has been found forthe input face. Thus, the process continues to block 1141 where anoutput is generated that includes the Max_score along with thePerson_ID, the Picture_ID and watchlist details. The output istransmitted to the combiner module 418. The process then returns toblock 1111 to await a next input. However, a Max_score that is notgreater than −1 signifies that a match has not been found for the inputface. Thus, the process continues to block 1143 where an output isgenerated that includes the Max_score set to −1. The output istransmitted to the combiner module 418. The process then returns toblock 1111 to await a next input.

As described above, in some embodiments, an output from the featurematcher module 416 is transmitted to the combiner module 418. The outputfrom block 1141 or block 1143 of the feature matcher module 416, asdescribed with respect to FIG. 11 is received as a match input by thecombiner module 418. In some embodiments, the match inputs are receivedfrom a plurality of instances of the feature matcher module 416. FIG. 12illustrates an operational procedure for an embodiment of the combinermodule 418 with respect to processing the received match inputs. Theprocedure begins, upon receiving a match input, at block 1201 where akey is created using a face ID and a frame ID associated with the matchinput. The face_ID and frame ID are generated by the face detectionmodule 306 and the imaging system 172, respectively during theprocessing. Each frame can have one or more faces. Each of these facesis being processed in parallel on partial watchlist data by the featurematcher module 416. A key is created using face ID and frame ID for thecombiner to know when all partial matches have been completed andreceived. For example, if there are two parallel feature matcher modules416 then the combiner should receive two results for a key correspondingto that face ID and frame ID. The match input is mapped to the key atblock 1203. The combiner module 418 checks if all results (e.g., all thematch inputs from the various instances of the feature matcher module416) have been received at block 1205. If the combiner module 418 hasnot received all the results, the procedure continues to block 1207 andwaits for a next match input to be received, thus returning to block1201.

However, once all the results have been received, the procedurecontinues from block 1205 to block 1209, where Max_score variable andthe Max_score_idx variable are both initialized to zero. At block 1211,the combiner module 418 retrieves the match results using the key. Atblock 1213, the procedure checks if match results remain to beprocessed. As long as match results remain to be processed the procedureexecutes the processing loop from block 1213 through block 1221. Thus,at block 1215, the next match result is retrieved.

The combiner module 418 reads the Max_score included in the match resultat block 1217. The Max_score included in the match result is thesimilarity score determined at block 1135 of FIG. 11 and output by block1141. Thus, to avoid confusion with the Max_score initialized at block1209, the Max_score included in the match result will be referred to asthe similarity score hereinafter. If the similarity score is greaterthan the Max_score, as determined at block 1219, the procedure continuesto block 1221. At block 1221, the Max_score is updated to reflect thesimilarity score and the Max_score_idx is updated with index of thecurrent match result. The procedure returns to block 1213 to beginprocessing the next match result, if one exists. However, if thesimilarity score is not greater than the Max_score, as determined atblock 1219, the procedure returns to block 1213 without updating thevalues of the Max_score and the Max_score_idx.

Once all the match results have been processed by the loop from block1213 through block 1221, the procedure continues to block 1223. At block1223, the Max_score is evaluated. If the Max_score is less than zero,all match results associated with the key are erased at block 1225. Theprocedure waits for next match inputs at block 1227, which returns theprocedure to block 1201. However, a Max_score greater than zero resultsin details of the matched face being output to the face annotator 176(shown in FIG. 1) at block 1229.

Additionally, the combiner module 418 obtains a current time at block1231. A registered score and timestamp for the individual are obtainedfrom memory, such as, for example, cache memory, at block 1233. Avariable, such as, for example, a variable named “send”, is initializedto a Boolean value of “false”. The “send” variable tracks whether thematched face is to be sent to the alerts modifier 124 (shown in FIG. 1)as well. The current time and the registration timestamp are compared atblock 1237. If the delta (e.g., the difference) between the timestampand the current time is greater than a predefined interval, theprocedure continues to block 1241 where the “send” variable is updatedto a Boolean value of “true”.

Returning to block 1237, if the delta between the timestamp and thecurrent time is not greater than the predefined interval, the procedurecontinues to block 1239 instead. At block 1239, the Max_score iscompared to a similarity score of a previous facial match for theindividual. If the current Max_score exceeds the previous similarityscore by a predefined amount, the procedure continues to block 1241where the “send” variable is updated to a Boolean value of “true”. Inthe case where the current Max_score does not exceeds the previoussimilarity score by the predefined amount, the procedure continues toblock 1243 without having the “send” variable updated to the Booleanvalue of “true”. Also, in the previously described cases where theprocedure continued to block 1241, either by way of block 1237 or block1239, the procedure continues to block 1243 as well. At block 1243, theregistration time is updated for the matched individual in the cache,and the registered score is updated to the current Max_score if the“send” variable is set to “true”, or if the matched individual is beingregistered for the first time. Otherwise, the previous similarity scoreis maintained in the cache for the matched individual.

At block 1245 the “send” variable is checked. If the “send” variable isset to “false”, the procedure continues directly to block 1225 where allmatch results associated with the key are erased. The procedure waitsfor next match inputs at block 1227, which returns the procedure toblock 1201. However, if the “send” variable is set to “true”, theprocedure continues to block 1247 where the matched face details aretransmitted to the alerts modifier 124. Once the transmission to thealerts modifier 124 is completed, the procedure continues to block 1225,erasing all match results associated with the key. The procedure waitsfor next match inputs at block 1227, which returns the procedure toblock 1201.

The process shown in FIG. 12 allows the worker unit to reduce thenetwork traffic by limiting the frequency that a same facial data istransmitted to the alert modifier 124. However, if the facial dataacquired is of, for example, a different angle that results in anincreased similarity score, the worker unit can transmit the new data tothe alert modifier 124 even if the interval has not been surpassed.

Turning to FIG. 13, an operational procedure of an embodiment of anannotation module 518 (shown in FIG. 5) of the face annotation unit 176(shown in FIG. 1), is illustrated. The annotation module 518 combinesinputs received from the detection input block 502, the match inputblock 506 and the frame input block 504 as described above with respectto FIG. 5. The procedure begins with the receipt of an input. At block1301 the procedure determines if the received input is a face detectionfrom the detection input block 502. If the input is received from thedetection input block 502, the procedure retrieves a timestamp from theinput at block 1303. The annotation module 518 checks at block 1305 ifan associated frame corresponding to the timestamp exists in cache. Ifan associated frame does not exist, the annotation module 518 proceedsto block 1309 to wait for a next input, thus returning the procedure toblock 1301. However, if an associated frame does exist in cache, theprocedure extracts the facial details from the input and adds thedetails to the cache at block 1307. Upon completion of block 1307, theannotation module 518 proceeds to block 1309 to wait for a next input,thus returning the procedure to block 1301.

However, if at block 1301, the input is determined to not be receivedfrom the detection input block 502, a check is performed, at block 1311,to determine if the input is received from the match input block 506. Ifthe input is received from the match input block 506, the procedureretrieves a timestamp from the input at block 1313. The annotationmodule 518 checks at block 1315 if an associated frame corresponding tothe timestamp exists in cache. If an associated frame does not exist,the annotation module 518 proceeds to block 1309 to wait for a nextinput, thus returning the procedure to block 1301. However, if anassociated frame does exist in cache, the procedure extracts the facialdetails from the input and adds the details to the cache at block 1317.Upon completion of block 1317, the annotation module 518 proceeds toblock 1309 to wait for a next input, thus returning the procedure toblock 1301.

If the received input is not from the match input block 506, theannotation module 518 continues from block 1311 to 1319, where theprocedure determines whether the input is from the frame input block504. If the input is not received from the frame input block, theprocedure waits for a next input and returns to block 1301. However, ifthe input is received from the frame input block 504, a timestamp isretrieved from the input at block 1321. At block 1323, the proceduredetermines if the received frame is in order, such that the timestamp ofthe received frame is correct and follows after the previously receivedframe input. If the received frame fails the check at block 1323, theprocedure continues to block 1309 to wait for a next input, thusreturning the procedure to block 1301.

If, however the received frame input passes the check at block 1323, theframe is extracted and stored in cache at block 1325. Previous faces areextracted from cache at block 1327. Additionally, at block 1329, facesfrom the cache are cleared in order to prepare to render the currentframe and get ready for a next frame. A check is performed, at block1331, to determine if any faces exist to be processed from the facesextracted from the cache. If the check fails, the procedure evicts theexpired frames at block 1333. An alert is sent for any faces that do notmatch individuals on the watchlists at block 1335. Alerts are sent forfaces that are above a certain quality threshold, such that the detectedface quality is sufficient for matching, but no match was found in thewatchlists.

Continuing to block 1337, the annotation module 518 renders the frame,annotates the frame with bounding boxes around the faces, along withnames of individuals matched in the watchlists around the bounding boxand publishes the annotated frames. The procedure continues to block1339 to wait for a next input, thus returning the procedure to block1301.

However, if faces exist for processing, as determined in block 1331, theprocedure retrieves the next face for processing at block 1341. At block1343 the annotation module 518 checks if the face is within the trackingwindow. Faces the are determined to be outside the tracking window arediscarded and the procedure returns to block 1331, continuing asdescribed above. Faces that are within the tracking window are trackedin the current frame using template matching and a new location for theface is determined at block 1345. At block 1347, a check is performed todetermine if a new location based on the tracking is found. If the checkat block 1347 fails, the procedure returns to block 1331 to continue theprocess as described above.

If new location is found at block 1347, the annotation module 518identifies any overlaps between the current face and any other face. Theoverlap is identified based on the location of the face and a presetthreshold. At block 1351, a check is performed to determine if anoverlap exists. If an overlap exists, the face details in the cache areupdated and the procedure returns to block 1331. If an overlap does notexist, the procedure continues from block 1351 to block 1355 where thenew face is added to the cache. The procedure returns to block 1331. Theloop including block 1331 and 1341 through 1355 is performed until thecheck performed at block 1331 determines that no more faces exist to beprocessed, at which point the procedure continues with blocks 1333through 1339 as described above.

FIG. 14 illustrates a process executed by embodiments of the presentinvention when a new worker unit 160 is added to the system 100 shown inFIG. 1. The process for connecting a new worker unit 160 to the masterunit 102 begins at block 1401. At block 1401, the new worker unit 160obtains the address for the master unit 102. The address can include, insome embodiments, an Internet protocol (IP) address and port number onwhich the security system APIs are exposed. In some embodiments, theworker unit 160 obtains the address by way of an interface provided toan administrator, such that the administrator enters the address andlogin credentials manually through a command line tool or graphical userinterface. In other embodiments, the address is obtained by way of anautomated negotiation procedure.

At block 1403 the worker unit 160 receives an authentication token and asecure socket layer (SSL) certificate from the master unit 102. In otherembodiments the SSL certificate is replaced with a transport layersecurity (TLS) certificate. The authentication token and certificateprovide a secured, encrypted communication channel between the masterunit 102 and the worker unit 160. In some embodiments, the watchlist andother data transmitted from the master unit 102 to the worker unit 160can be encrypted using a public-private key technique. Moreover, datacan also be encrypted when sent from the worker unit 160 to the masterunit 102 using a similar public-private key technique.

At block 1405 the worker unit 160 registers with the master unit 102 byproviding authentication token, unique device identifier, such as, forexample, a media access control (MAC) address, a user assigned name,etc., IP address, and hardware configuration to the master unit 102. Thehardware configuration information transmitted to the master unit 102can include processor information, available memory, available storagespace, and imaging information, e.g. resolution, color or monochrome,frame rate, gain, etc. In some embodiments, the worker unit 160 caninclude a global positioning system (GPS) receiver, and thus the workerunit 160 can provide GPS coordinates to the master unit. In otherembodiments, a location description of the worker unit 160 is providedto the master unit 102 manually by an administrator. The locationdescription can include, for example a room identifier, such as frontentrance, office, conference room, and the like. In still otherembodiment, both GPS coordinates and room identifier can be provided tothe master unit 102.

Once the worker unit 160 is registered with the master unit 102 inaccordance with block 1405, the worker unit 160 initiates a backgroundprocess at block 1407. The background process provides all furthercommunication between the worker unit 160 and the master unit 102 using,for example, the authentication token. The background process, in someembodiments, provides periodic status updates of the worker unit 160 tothe master unit 102, and receives watchlist updates and operationalcommands from the master unit 102.

FIG. 15 illustrates operation of an embodiment of the background processinitiated at block 1407 of FIG. 14. At block 1501, the backgroundprocess of the worker unit 160 checks with the master unit 102 forcommands/requests instructing the worker unit 160 to start and/or stopany module instances. If a command is received to either start or stop amodule instance, the background process proceeds to block 1503. At block1503, the background process executes the start/stop command for theidentified module instance. In some embodiments, a start command canrequire download of individual modules and configuration files from themaster unit 102 if not already present on the worker unit 160. Once thenecessary components are present on the worker unit 160, the moduleinstance is started.

Since the master unit 102 can be configured to download modules to theworker unit 160, the system 100 can ensure that all the worker units 160registered with the master unit 102 are executing the same versions ofthe various modules described above. Thus, when a module is updated, theupdate can be propagated from the master unit 102 to all the workerunits 160 within a short period of time.

After the start/stop instructions have been successfully executed, thebackground process proceeding to block 1505, where the process waits fora predetermined interval before continuing to block 1507. If at block1501 no commands are received from the master unit 102, the backgroundprocess continues directly to block 1505 as well. At block 1507, thebackground process transmits a status update to the master unit 102providing the current status of the worker unit 160. The status updatecan include, for example, a status of module instances, hardwareresources being utilized, hardware resource available, etc. Thebackground process returns to block 1501 and continues as previouslydescribed.

Turning to FIG. 16, a flow diagram is shown illustrating a processexecuted by some embodiment of the master unit of the present invention.The master unit, such as, for example master unit 102 shown in FIG. 1,transmits, at block 1551, a selected portion of biometrics data as awatchlist to each worker unit, such as worker unit 160 shown in FIG. 1.The portion of biometrics data transmitted to each worker unit isselected in response to respective characteristic data received fromeach worker unit. In some embodiments, the worker unit 160 can initiatethe initial watchlist download by transmitting a watchlist request tothe master unit 102 during registration of the worker unit 160 with themaster unit 102, as shown in FIG. 14 and FIG. 15. In other embodiments,the master unit 102 initiates the initial watchlist download as part ofthe registration of the worker unit 160.

Additionally, the master unit 102 checks periodically at block 1551whether changes have been made to the biometrics database 122. If achange has been made to the biometrics database 122, the master unit 102updates the watchlist transmitted to the worker unit 160 in response tothe changes. In environments where the worker unit 160 is not assigned astatic network address and/or the network connectivity is intermittentor unreliable, the watchlist updates can also be initiated by an updaterequest transmitted by the worker unit 160 to the master unit 102.

At block 1553, the master unit 102 receives facial recognition data fromeach worker unit 160. The facial recognition data includes a person ofinterest with an associated match confidence value calculated by eachworker unit 160 based on respective watchlists received by each workerunit 160. The master unit 102 determines if the match confidence valueexceeds a match confidence threshold at block 1555. If the matchconfidence value exceeds or is equal to the match confidence threshold,the process continues to block 1557 where an alert is issued.

However, if the match confidence value is below the match confidencethreshold, the process proceeds to block 1559. At block 1559, the masterunit 102 calculates a combined match confidence value between a sameperson of interest identified in multiple facial recognition datareceived from each worker unit 160 and the biometrics data associatedwith an individual. The master unit 102 continues to block 1561 todetermine if the combined match confidence value is equal to or exceedsthe match confidence threshold. If the combined match confidence valueis at least equal to the match confidence threshold at block 1561, theprocess continues to block 1557 where an alert is issued for the personof interest. However, a combined match confidence value below the matchconfidence threshold causes the process to discard the facialrecognition data for the person of interest at block 1563.

After the master unit 102 has either issued an alert at block 1557 ordiscarded the facial recognition data at block 1563, the process returnsto block 1553. The master unit 102, thus, waits to receive new facialrecognition data from the worker units 160.

FIG. 17 illustrates an environment utilizing an embodiment of thepresent invention. The environment shown in FIG. 17 can be a publicfacility, such as for example, an airport, or courthouse, a commercialfacility, or a park. The particular environment illustrated is anairport terminal 1600. The airport terminal 1600 includes a first workerunit 1602 monitoring a restricted space 1604. The restricted space 1604can be any area or room in which access is limited to a known group ofindividuals, such as, for example, security personnel, maintenancepersonnel, etc. The master unit 1606 can be installed in one suchrestricted area 1604, for example, in a security office. Additionally,the airport terminal 1600 includes a second worker unit 1608 positionedto monitor a public-accessible area 1610. The public-accessible area1610 can be, for example, the space outside the security checkpoint1612. A third worker unit 1614 can be positioned to monitor the spacebeyond the security checkpoint 1612 leading to individual gates, forexample, the space beyond the security checkpoint 1612, will be referendto hereinafter as a secured area.

Regarding the first worker unit 1602, a first watchlist is transmittedfrom the master unit 1606 to the first worker unit 1602 that includesfacial information for individuals that are permitted to enter therestricted area 1604. Thus, the first worker unit 1602 executes theprocess described above such that an individual that is not on the firstwatchlist is considered a person of interest. Consequently, when thefirst worker unit 1602 observers a person of interest (an individual noton the first watchlist) entering the restricted area 1604, the firstworker unit 1602 sends the unmatched face data (such as the unmatchedface data 524 shown in FIG. 5) to the master unit 1606, which in turncan issue an alert.

The second worker unit 1608 and the third worker unit 1614, on the otherhand, are monitoring spaces open to the general public. Thus, a secondwatchlist received by the second worker unit 1608 and the third workerunit 1614 from the master unit 1606 includes facial data of individualsthat are persons of interest, e.g., individuals that may be wanted byauthorities or otherwise barred from entering the location.Consequently, when the second worker unit 1608, for example, observes aperson of interest (an individual on the second watchlist) in thepublic-accessible area 1610, the second worker unit 1608 sends thematched face data (such as the facial match data 420 shown in FIG. 4) tothe master unit 1606, which in turn can issue an alert.

In some embodiments, the third worker unit 1614 can be configured toreceive the second watchlist that include persons of interest, e.g.,individuals that may be wanted by authorities, and a third watchlistthat includes individuals that are permitted to be beyond the securitycheckpoint 1612. The third watchlist can be a watchlist generated bysecurity personnel at the security zone 1612. As an individual ispermitted to cross the security checkpoint 1612, the facial data andother relevant information is transmitted to the master unit 1606, wherethe facial data and other relevant information is added to the thirdwatchlist. The third watchlist can then be transmitted to the thirdworker unit 1614 and any other worker units in the secured area. Anindividual on the third watchlist can be removed therefrom by the masterunit when the individual is observed leaving the secured area, forexample, by boarding an airplane. In this way, the third worker unit1614 ensures that only individuals that have successfully crossed thesecurity checkpoint 1612 are in the secured area; all other individualswill trigger an alert.

In other embodiments implemented at an airport terminal, fourthwatchlists can be generated by the master unit 1606 that includeindividuals that allowed to board specific flights. The fourthwatchlists can be generated by the master unit 1606 based on datareceived from the security checkpoint 1612 since a boarding pass,listing flight details, can be presented to the security personnelstationed there. The fourth watchlists can then be transmitted by themaster unit 1606 to the appropriately located worker units monitoringthe respective gates.

Embodiments described herein may be entirely hardware, entirely softwareor including both hardware and software elements. In a preferredembodiment, the present invention is implemented in software, whichincludes but is not limited to firmware, resident software, microcode,etc.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable storage medium such as a semiconductor or solid-statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk and anoptical disk, etc.

Each computer program may be tangibly stored in a machine-readablestorage media or device (e.g., program memory or magnetic disk) readableby a general or special purpose programmable computer, for configuringand controlling operation of a computer when the storage media or deviceis read by the computer to perform the procedures described herein. Theinventive system may also be considered to be embodied in acomputer-readable storage medium, configured with a computer program,where the storage medium so configured causes a computer to operate in aspecific and predefined manner to perform the functions describedherein.

A data processing system suitable for storing and/or executing programcode may include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code to reduce the number of times code is retrieved frombulk storage during execution. Input/output or I/O devices (includingbut not limited to keyboards, displays, pointing devices, etc.) may becoupled to the system either directly or through intervening I/Ocontrollers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

As employed herein, the term “hardware processor subsystem” or “hardwareprocessor” can refer to a processor, memory, software or combinationsthereof that cooperate to perform one or more specific tasks. In usefulembodiments, the hardware processor subsystem can include one or moredata processing elements (e.g., logic circuits, processing circuits,instruction execution devices, etc.). The one or more data processingelements can be included in a central processing unit, a graphicsprocessing unit, and/or a separate processor- or computing element-basedcontroller (e.g., logic gates, etc.). The hardware processor subsystemcan include one or more on-board memories (e.g., caches, dedicatedmemory arrays, read only memory, etc.). In some embodiments, thehardware processor subsystem can include one or more memories that canbe on or off board or that can be dedicated for use by the hardwareprocessor subsystem (e.g., ROM, RAM, basic input/output system (BIOS),etc.).

In some embodiments, the hardware processor subsystem can include andexecute one or more software elements. The one or more software elementscan include an operating system and/or one or more applications and/orspecific code to achieve a specified result.

In other embodiments, the hardware processor subsystem can includededicated, specialized circuitry that performs one or more electronicprocessing functions to achieve a specified result. Such circuitry caninclude one or more application-specific integrated circuits (ASICs),field-programmable gate arrays (FPGAs), and/or programmable logic arrays(PLAs).

These and other variations of a hardware processor subsystem are alsocontemplated in accordance with embodiments of the present invention.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the embodiment is included in at least one embodiment ofthe present invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment. However, it is to beappreciated that features of one or more embodiments can be combinedgiven the teachings of the present invention provided herein.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended for as many items listed.

The foregoing is to be understood as being in every respect illustrativeand exemplary, but not restrictive, and the scope of the inventiondisclosed herein is not to be determined from the Detailed Description,but rather from the claims as interpreted according to the full breadthpermitted by the patent laws. It is to be understood that theembodiments shown and described herein are only illustrative of thepresent invention and that those skilled in the art may implementvarious modifications without departing from the scope and spirit of theinvention. Those skilled in the art could implement various otherfeature combinations without departing from the scope and spirit of theinvention. Having thus described aspects of the invention, with thedetails and particularity required by the patent laws, what is claimedand desired protected by Letters Patent is set forth in the appendedclaims.

What is claimed is:
 1. A method for distributed real-time securitymonitoring and alerting, comprising: transmitting a selected portion ofbiometrics data as a watchlist to each worker unit of a plurality ofworker units, the portion of biometrics data being selected in responseto respective characteristic data received from each worker unit, alocation for the characteristic data being set as a relative positionfor at least one of the plurality of worker units; receiving facialrecognition data from each worker unit, the facial recognition dataincluding a person of interest with an associated match confidence valuecalculated by each worker unit based on respective watchlists receivedby each worker unit; and calculating a combined match confidence valuebetween a same person of interest identified in multiple facialrecognition data received from each worker unit and the biometric dataassociated with an individual, the combined match confidence value beingcalculated in response to match confidence values associated with thesame person of interest in respective facial recognition data beingbelow a match confidence threshold, the match confidence threshold beingdetermined based on a different worker unit confidence threshold and amaster unit confidence threshold, the worker unit confidence being setat a lower value than the master unit confidence threshold.
 2. Themethod as in claim 1, further comprising updating the watchlisttransmitted to each worker unit in response to changes made to thebiometrics data.
 3. The method as in claim 1, wherein receivingcharacteristic data includes receiving respective characteristic datafrom each of the plurality of worker units.
 4. The method as in claim 3,wherein: a first characteristic data of a first worker unit of theplurality of worker units identifying the monitoring focus of the firstworker unit as a public space, and the watchlist transmitted to thefirst worker unit including biometric data for persons of interest, anda second characteristic data of a second worker unit of the plurality ofworker units identifying the monitoring focus of the second worker unitas a secured space, and the watchlist transmitted to the second workerunit including biometric data for individuals authorized to enter thesecured space.
 5. The method as in claim 1, further comprising issuingan alert in response to at least one of a match confidence value or acombined match confidence value at least equaling a match confidencethreshold.
 6. The method as in claim 1, wherein a first facialrecognition data and a second facial recognition data, each includingthe same person of interest having a match confidence value below thematch confidence threshold, is received from a same worker unit, thefirst facial recognition data and the second facial recognition databeing associated with respective image frames recorded at differenttimes; and the combined match confidence value is calculated based onthe first facial recognition data and the second facial recognition datareceived from the same worker unit.
 7. The method as in claim 1, whereina first facial recognition data, received from a first worker unit, anda second facial recognition data, received from a second worker unit,each include the same person of interest having a match confidence valuebelow the match confidence threshold; and the combined match confidencevalue is calculated based on the first facial recognition data and thesecond facial recognition data received from the first worker unit andthe second worker unit.
 8. A method for distributed real-time securitymonitoring and alerting, comprising: transmitting characteristic dataidentifying at least a location and monitoring focus of a plurality ofworker units to a management server, a location for the characteristicdata being set as a relative position for at least one of the pluralityof worker units; receiving at least one watchlist from the managementserver, the watchlist including biometric data of individualsrepresenting a portion of biometric data managed by the managementserver selected based on the characteristic data; identifying a personof interest using facial recognition based on the biometric data of thewatchlist received from the management server; labeling the person ofinterest in the image data including calculating a match confidencevalue between a same person of interest identified in multiple facialrecognition data received from each worker unit and the biometric dataassociated with an individual, the combined match confidence value beingcalculated in response to match confidence values associated with thesame person of interest in respective facial recognition data beingbelow a match confidence threshold, the match confidence threshold beingdetermined based on a different worker unit confidence threshold and amaster unit confidence threshold, the worker unit confidence being setat a lower value than the master unit confidence threshold; andtransmitting an alert request including the image data having a labeledperson of interest to the management server.
 9. The method as in claim8, wherein identifying a person of interest further includes:identifying individual faces in an image frame captured by an imagingsystem; and matching the individual faces with individuals using thewatchlist received from the management server.
 10. The method as inclaim 8, wherein issuing an alert request further includes annotatingeach face identified as a person of interest in an image frame prior totransmitting the facial recognition data to the management server, eachface identified as a person of interest being annotated with anidentifier associated with an individual on the watchlist and a matchconfidence value calculated based on the biometric data in thewatchlist.
 11. The method as in claim 8, wherein issuing an alertrequest further includes annotating each face identified as a person ofinterest in an image frame prior to transmitting the facial recognitiondata to the management server, each face identified as a person ofinterest being annotated with an identifier indicating that the face isunmatched with an individual on the watchlist based on the biometricdata in the watchlist.
 12. A system for distributed security monitoringand alerting, comprising: one or more worker units including an imagingsystem, and positioned to monitor individuals in a space; and amanagement server configured to: transmit a selected portion ofbiometrics data as a watchlist to each worker unit of the one or moreworker units, the portion of biometrics data being selected in responseto respective characteristic data received from each worker unit, alocation for the characteristic data being set as a relative positionfor at least one of the one or more worker units, receive facialrecognition data from each worker unit, the facial recognition dataincluding a person of interest with an associated match confidence valuecalculated by each worker unit based on respective watchlists receivedby each worker unit, calculate a combined match confidence value betweena same person of interest identified in multiple facial recognition datareceived from each worker unit and the biometric data associated with anindividual, the combined match confidence value being calculated inresponse to match confidence values associated with the same person ofinterest in respective facial recognition data being below a matchconfidence threshold, the match confidence threshold being determinedbased on a different worker unit confidence threshold and a master unitconfidence threshold, the worker unit confidence being set at a lowervalue than the master unit confidence threshold, and issue an alert inresponse to at least one of a match confidence value or a combined matchconfidence value at least equaling the match confidence threshold. 13.The system as in claim 12, wherein the management server is furtherconfigured to update the watchlist transmitted to each worker unit inresponse to changes made to the biometrics data.
 14. The system as inclaim 12, wherein: a first characteristic data of a first worker unitidentifies the monitoring focus of the first worker unit as a publicspace, and the watchlist transmitted to the first worker unit includesbiometric data for persons of interest, and a second characteristic dataof a second worker unit identifies the monitoring focus of the secondworker unit as a secured space, and the watchlist transmitted to thesecond worker unit includes biometric data for individuals authorized toenter the secured space.
 15. The system as in claim 14, wherein a firstfacial recognition data and a second facial recognition data, eachincluding the same person of interest having a match confidence valuebelow the match confidence threshold, is received from a same workerunit, the first facial recognition data and the second facialrecognition data being associated with respective image frames recordedat different times; and the combined match confidence value iscalculated based on the first facial recognition data and the secondfacial recognition data received from the same worker unit.
 16. Thesystem as in claim 14, wherein a first facial recognition data, receivedfrom a first worker unit, and a second facial recognition data, receivedfrom a first worker unit, each include the same person of interesthaving a match confidence value below the match confidence threshold;and the combined match confidence value is calculated based on the firstfacial recognition data and the second facial recognition data receivedfrom the first worker unit and the second worker unit.
 17. The system asin claim 12, wherein each of the one or more worker units includes: aface detection module configured to identify individual faces in animage frame captured by the imaging system; and a face matching moduleconfigured to match faces detected by the face detection module with theone or more individuals using the watchlist received from the managementserver.
 18. The system as in claim 17, wherein the each of the one ormore worker units includes an annotation module configured to annotateeach face identified as a person of interest in an image frame prior totransmitting the facial recognition data to the management server, eachface identified as a person of interest being annotated with anidentifier and a match confidence value calculated based on thebiometric data in the watchlist.
 19. The system as in claim 17, whereinthe annotation module generates a match confidence value responsive to afirst facial recognition data of a person of interest identified in afirst image frame and a second facial recognition data of the person ofinterest identified in a second image frame recorded at a different timethan the first image frame.
 20. The system as in claim 17, wherein theeach of the one or more worker units includes an annotation moduleconfigured to annotate each face identified as a person of interest inan image frame prior to transmitting the facial recognition data to themanagement server, each face identified as a person of interest beingannotated with an identifier indicating that the face is unmatched withindividuals on the watchlist.